Considerable advances have been made in the development of targeted therapies for the treatment of cancer and other diseases. Such targeted therapies include monoclonal antibodies that bind to antigens that are specifically or preferentially expressed on tumor cells and small molecule drugs that specifically interfere with discrete components of signaling pathways active in tumor cells. For example, cetuximab (Erbitux®) is a monoclonal antibody that targets the epidermal growth factor receptor (EGFR, also known as ErbB1 or HER1) that is expressed in at least certain colon cancers and head and neck cancers. Also for example, imatinib (Gleevec®) is a small molecule that targets the BCR-Abl tyrosine kinase, which is expressed, and acts as an oncogenic factor, in certain chronic myeloid leukemias and is an abnormal variant of a benign cellular protein. While such targeted therapies have been shown to be effective in some patients, the response rate is never 100%. For example, the average response rate for cetuximab monotherapy is only around 15-20% of patients, even when tumors are known to express ErbB1 (EGFR). Thus, mere expression of ErbB1 (the antigen targeted by the cetuximab antibody) in a tumor does not guarantee responsiveness to cetuximab.
Thus, while targeted therapies are very promising, the variable response rate of patients to such therapies, combined with the side effects associated with such therapies and the typical high cost of such therapies, indicates that methods for treating patients which involve predicting which patients are likely to respond to therapeutic treatment and only administering the treatment to patients who are predicted to respond are highly desirable. One approach that has been taken has been to try to identify genetic markers (e.g., mutations or alleles) that correlate with responsiveness to therapy. In this approach, a sample from the patient is genotyped prior to treatment to determine whether the patient carries a genetic marker(s) that is indicative of responsiveness to therapy. Another approach that has been taken is to try to identify protein biomarkers that correlate with responsiveness to therapy. In this approach, protein expression is determined in a sample from the patient prior to treatment to determine whether the patient expresses one or more protein biomarkers that are indicative of responsiveness to therapy.
Both of the aforementioned approaches can be considered to be “direct” marker approaches, wherein the presence (or absence, or level of expression) of the marker(s) (e.g., BCR-Abl or ErbB1) directly being measured has been demonstrated to correlate with responsiveness or non-responsiveness to therapy. Furthermore, both of these approaches rely on the use of markers that are sufficiently stable in cells such that they can be reliably measured or quantitated in a sample that has been isolated from the patient. Given that there may be a considerable time lag between when a sample is isolated from a patient and when the marker(s) is measured in the sample, such “direct” marker approaches described above typically require the use of genetic or protein markers that are not subject to degradation or alteration over time when samples are subjected to conventional processing and handling. While such stable, “direct” markers that are predictive of responsiveness to certain therapeutic agents have been identified, it is unclear whether such markers can be identified for all therapeutic agents.
It is thought that tumors are driven to grow by a set of ligand activated signaling pathways, which are usually activated by ligands binding to their cognate receptors, inducing the phosphorylation of the receptor itself as well as of downstream kinases, leading to further phosphorylation of downstream components of the pathway. These kinases trigger cell survival and proliferation. Accordingly, activation of the signaling pathway leads to alteration of intracellular components, in particular protein phosphorylation. The phosphorylation signature of the receptors expressed on tumor tissue can help to identify the main pathways that drive a particular tumor's progression. However, phosphoproteins can be very labile and the phosphorylation can dissipate quickly after surgery if the tissue sample is not immediately and rapidly frozen (or, in some cases, formalin fixed). Moreover, even where it is possible to reliably measure levels of one or more phosphoproteins in a sample of a particular tumor, the predictive value of the presence or absence of any particular phosphoprotein regarding efficacy of treatment of such a tumor with any particular therapeutic agent is generally unknown. Therefore, while phosphoprotein profiles contain important information about the pathways driving tumor progression, such phosphoprotein profiles currently are not widely used as biomarkers for predicting responsiveness to therapeutic treatment.
Accordingly, new methods for determining levels of various phosphoproteins and of using such levels and other tumor cell characteristics for predicting the responsiveness of individual tumors to particular therapeutic agents are needed to improve the therapeutic and cost effectiveness of cancer therapies.